MAT1

Based on the mat1_1_1 and mat1_1_2 loci from Leptographium procerum, I did depth assays to see presence/absence of genes.

After searching for the homolog proteins, I found that the regions of CMW154_Contig94:9951-11159 that includes the two proteins LWAP_08037-RA and LWAP_08038-RA that are the best BLAST hits to the MAT1_1_1 gene and MAT1_2_1

Lets see how they look.

mat1 <- readRDS("mat1_depth.Rds")
mat1.pop2 <- readRDS("mat1_others.Rds")

colnames(mat1) <- gsub(colnames(mat1), pattern = "_dupmrk.bam", replacement = "")

mat1 <- merge(mat1, mat1.pop2, by = c("Chrom","Pos"))

library(reshape2)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.1     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(viridis)

## Loading required package: viridisLite

mat1.melt <- melt(mat1, id=c("Chrom","Pos"))

We have 3 genes: MAT1-1-1, MAT1-2-1 and the SLA gene.

• In the MAT1 type we can find: Complete SLA, Complete MAT1-1-1, and Complete MAT1-2-1.
• In the MAT2 type we can find: Complete SLA, Truncated MAT1-1-1, and Absent MAT1-2-1.

Lets see how this looks like:

sla <- mat1[mat1$Pos > 14590 & mat1$Pos < 17898, -c(1,2)]
mat1_1_1 <- mat1[mat1$Pos > 11159 & mat1$Pos < 13083, -c(1,2)]
mat1_2_1 <- mat1[mat1$Pos > 9951 & mat1$Pos < 10800, -c(1,2)]

sla.avg <- apply(sla, 2, function (y){ sum(y > 0) })/nrow(sla) * 100 
sla.avg <- tibble("sample"=names(sla.avg),sla.avg)

mat1_1_1.avg <- apply(mat1_1_1, 2, function (y){ sum(y > 0) })/nrow(mat1_1_1) * 100 
mat1_1_1.avg <- tibble("sample"=names(mat1_1_1.avg),mat1_1_1.avg)

mat1_2_1.avg <- apply(mat1_2_1, 2, function (y){ sum(y > 0) })/nrow(mat1_2_1) * 100
mat1_2_1.avg <- tibble("sample"=names(mat1_2_1.avg),mat1_2_1.avg)     

all.cov <- merge(sla.avg,mat1_1_1.avg, by = "sample" ) %>% merge(.,mat1_2_1.avg, by = "sample" )
all.cov.m <- as.matrix(all.cov[,-1])
rownames(all.cov.m) <- all.cov[,1]

vcfR::heatmap.bp(all.cov.m)

So, in that case:

Mating type 1:

mat1 <- all.cov[all.cov$sla.avg > 99 & all.cov$mat1_1_1.avg > 99 & all.cov$mat1_2_1.avg > 99,]
mat1$mat <- "MAT_1"
mat2 <- all.cov[!(all.cov$sla.avg > 99 & all.cov$mat1_1_1.avg > 99 & all.cov$mat1_2_1.avg > 99),]
mat2$mat <- "MAT_2"

mat.ann <- rbind(mat1,mat2)
mat.ann$mat <- factor(mat.ann$mat)

mat.ann.raw <- mat.ann

library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

library(formattable)

mat.ann <- mat.ann %>% mutate(
    SLA = ifelse(sla.avg > 90,
                  cell_spec(sla.avg, color = "green", bold = T),
                  cell_spec(sla.avg, color = "red", italic = T)),
    MAT1_1_1= ifelse(mat1_1_1.avg > 90,
                  cell_spec(mat1_1_1.avg, color = "green", bold = T),
                  cell_spec(mat1_1_1.avg, color = "red", italic = T)),
    MAT1_2_1 = ifelse(mat1_2_1.avg > 90,
                  cell_spec(mat1_2_1.avg, color = "green", bold = T),
                  cell_spec(mat1_2_1.avg, color = "red", italic = T)),
    mat = color_tile("blue", "orange")(mat)
  )
mat.ann [,-c(2,3,4)]  %>% 
  kable(escape = F) %>%
  kable_styling("hover", full_width = F) %>%
  column_spec(5, width = "3cm")

sample	mat	SLA	MAT1_1_1	MAT1_2_1
CABL1	MAT_1	99.969761112791	99.8439937597504	100
CABL2	MAT_1	100	99.9479979199168	100
CABL3	MAT_1	100	99.9479979199168	100
CADC02	MAT_1	100	99.8439937597504	100
CADC03	MAT_1	99.969761112791	99.5839833593344	100
CADC06	MAT_1	99.9395222255821	99.6359854394176	100
MP01	MAT_1	100	99.9479979199168	100
MP03	MAT_1	100	99.8439937597504	100
MP04	MAT_1	100	100	100
MP06	MAT_1	100	100	100
MP08	MAT_1	100	99.9479979199168	100
MP09	MAT_1	100	99.9479979199168	100
MP10	MAT_1	100	99.8439937597504	100
STF1015-01	MAT_1	100	100	100
STF1015-09	MAT_1	100	100	100
STF1015-13	MAT_1	100	100	100
STF1015-14	MAT_1	99.6068944662836	100	100
STF1015-15	MAT_1	100	100	100
STF1032-07	MAT_1	100	100	100
STF1032-12	MAT_1	100	100	100
STF1032-13	MAT_1	100	100	100
STFO706-01	MAT_1	100	100	100
STFO706-04	MAT_1	100	100	100
STFO706-05	MAT_1	100	100	100
STFO7A2-02	MAT_1	100	100	100
STFO7A2-03	MAT_1	100	100	100
STFO7A2-04	MAT_1	100	100	100
STFO7A2-05	MAT_1	100	100	100
STFO7A2-06	MAT_1	99.6976111279105	100	100
STFO7A2-07	MAT_1	100	100	100
STFO7A2-11	MAT_1	100	100	100
STFO9A5-01	MAT_1	100	100	100
STFO9A5-02	MAT_1	100	100	100
STFO9A5-04	MAT_1	100	99.9479979199168	100
STFO9A5-07	MAT_1	100	99.9479979199168	100
STFO9A5-08	MAT_1	100	100	100
STFO9A5-10	MAT_1	100	99.6359854394176	100
WRTF5180-08	MAT_1	100	100	100
WRTF5180-09	MAT_1	100	100	100
WRTF5180-10	MAT_1	100	100	100
WRTF5180-11	MAT_1	100	99.9479979199168	100
WRTF5180-12	MAT_1	100	100	100
WRTF5360-01	MAT_1	100	100	100
WRTF5360-02	MAT_1	100	100	100
WRTF5360-03	MAT_1	100	100	100
WRTF5360-05	MAT_1	100	100	100
WRTF5360-06	MAT_1	100	100	100
WRTF5360-10	MAT_1	100	100	100
WRTF5360-11	MAT_1	100	100	100
WRTF5360-12	MAT_1	100	100	100
WRTF5480-01	MAT_1	99.969761112791	100	100
WRTF5480-03	MAT_1	100	100	100
WRTF5480-04	MAT_1	100	100	100
WRTF5480-05	MAT_1	99.969761112791	100	100
WRTF5480-07	MAT_1	99.969761112791	100	100
WRTF5480-12	MAT_1	100	99.9479979199168	100
WRTF5481B-02	MAT_1	100	100	100
WRTF5481B-07	MAT_1	100	100	100
WRTF5481B-13	MAT_1	100	100	100
WRTF5481B-15	MAT_1	100	100	100
WRTF8800-02	MAT_1	100	100	100
WRTF8800-03	MAT_1	100	100	100
WRTF8800-05	MAT_1	100	100	100
WRTF8800-06	MAT_1	100	100	100
WRTF8800-09	MAT_1	100	100	100
WRTF8800-12	MAT_1	99.969761112791	100	100
WSTF230-02	MAT_1	100	100	100
WSTF230-03	MAT_1	100	100	100
WSTF230-04	MAT_1	100	100	100
WSTF230-05	MAT_1	100	100	100
WSTF230-06	MAT_1	100	100	100
WSTF230-11	MAT_1	100	100	100
WSTF700-01	MAT_1	100	100	99.8820754716981
WSTF700-03	MAT_1	100	100	100
WSTF700-04	MAT_1	100	100	100
WSTF700-06	MAT_1	100	100	100
WSTF700-07	MAT_1	100	100	99.8820754716981
WSTF700-08	MAT_1	100	99.8959958398336	100
WSTF700-12	MAT_1	100	100	100
WSTF700-13	MAT_1	100	99.9479979199168	100
WSTF7280-01	MAT_1	100	100	100
WSTF7280-02	MAT_1	100	100	100
WSTF7280-03	MAT_1	100	100	100
WSTF7280-04	MAT_1	100	100	100
WSTF7280-05	MAT_1	100	100	100
WSTF7280-06	MAT_1	100	100	100
WSTF7280-07	MAT_1	100	100	100
WSTF7280-08	MAT_1	100	100	100
WSTF7280-09	MAT_1	100	100	100
WSTF7280-10	MAT_1	100	99.9479979199168	100
WSTF7280-11	MAT_1	100	100	100
WSTF7280-12	MAT_1	100	100	100
WSTF7280-13	MAT_1	100	100	100
WSTF730-01	MAT_1	100	100	100
WSTF730-02	MAT_1	100	100	100
WSTF730-03	MAT_1	100	100	100
WSTF730-04	MAT_1	100	100	100
WSTF730-05	MAT_1	100	99.8439937597504	100
WSTF730-06	MAT_1	100	99.7919916796672	100
WSTF730-07	MAT_1	100	100	100
WSTF730-08	MAT_1	100	100	100
WSTF730-09	MAT_1	100	100	100
WSTF730-10	MAT_1	100	100	100
WSTF730-11	MAT_1	100	100	100
WSTF770-01	MAT_1	100	100	100
WSTF770-02	MAT_1	100	99.8959958398336	100
WSTF770-03	MAT_1	100	100	100
WSTF770-05	MAT_1	100	99.9479979199168	100
WSTF770-08	MAT_1	100	100	100
WSTF770-09	MAT_1	100	100	100
WSTF770-10	MAT_1	100	99.9479979199168	100
MP02	MAT_2	100	85.2314092563703	0
MP05	MAT_2	100	85.3354134165367	0
MP11	MAT_2	100	87.1034841393656	0
STF1015-16	MAT_2	100	85.2834113364535	0
STF1015-19	MAT_2	100	85.2834113364535	0
STF1015-2X	MAT_2	100	85.2834113364535	0
STF1015-2Y	MAT_2	100	85.3354134165367	0
STF1032-01	MAT_2	100	85.2834113364535	24.0566037735849
STF1032-04	MAT_2	100	85.3874154966199	0
STF1032-05	MAT_2	100	87.207488299532	5.54245283018868
STF1032-08	MAT_2	100	87.7795111804472	0
STF1032-09	MAT_2	100	85.2834113364535	0
STF1032-10	MAT_2	100	85.3874154966199	0
STF1032-11	MAT_2	100	85.3354134165367	17.8066037735849
STFO706-06	MAT_2	100	85.2834113364535	0
STFO706-08	MAT_2	100	97.2438897555902	0
STFO706-09	MAT_2	100	85.3874154966199	0
STFO706-11	MAT_2	100	89.0275611024441	0
STFO706-16	MAT_2	100	85.2834113364535	0
STFO706-17	MAT_2	100	85.3874154966199	19.3396226415094
STFO706-18	MAT_2	100	85.3354134165367	0
STFO7A2-01	MAT_2	100	85.3874154966199	0
STFO7A2-08	MAT_2	100	85.3354134165367	0
STFO7A2-09	MAT_2	100	85.2834113364535	35.2594339622642
STFO9A5-06	MAT_2	100	85.2834113364535	0
STFO9A5-09	MAT_2	100	85.2834113364535	20.7547169811321
WRTF5180-01	MAT_2	100	85.2834113364535	0
WRTF5180-02	MAT_2	100	85.2834113364535	0
WRTF5180-03	MAT_2	100	85.2834113364535	0
WRTF5180-04	MAT_2	100	85.3354134165367	0
WRTF5180-06	MAT_2	100	90.3796151846074	9.31603773584906
WRTF5180-07	MAT_2	100	85.3354134165367	13.4433962264151
WRTF5360-07	MAT_2	100	85.3354134165367	0
WRTF5360-08	MAT_2	100	85.2834113364535	0
WRTF5360-09	MAT_2	100	85.2834113364535	0
WRTF5481B-01	MAT_2	100	85.2834113364535	0
WRTF5481B-03	MAT_2	100	85.2834113364535	0
WRTF5481B-05	MAT_2	100	90.275611024441	0
WRTF5481B-09	MAT_2	100	85.3874154966199	0
WRTF5481B-10	MAT_2	100	85.2834113364535	0
WRTF5481B-11	MAT_2	100	85.2834113364535	0
WRTF5481B-12	MAT_2	100	85.1274050962038	0
WRTF5481B-14	MAT_2	100	85.3354134165367	0
WRTF8800-01	MAT_2	100	85.2834113364535	0
WRTF8800-08	MAT_2	100	85.2834113364535	0
WRTF8800-10	MAT_2	100	85.2834113364535	0
WRTF8800-11	MAT_2	98.8811611732688	85.2834113364535	35.6132075471698
WSTF230-09	MAT_2	100	85.2834113364535	27.8301886792453
WSTF230-13	MAT_2	100	93.7077483099324	25.5896226415094
WSTF700-10	MAT_2	100	96.0478419136765	0
WSTF770-04	MAT_2	100	85.2834113364535	0
WSTF770-06	MAT_2	100	85.2314092563703	0
WSTF770-11	MAT_2	100	85.3354134165367	0

Looking at the raw depth of coverage:

MAT1

colnames(mat1.melt) <- c("Chrom","Pos","sample","Depth")
mat1.melt <- merge(mat1.melt, mat.ann.raw[,c(1,5)], by = "sample")

ggplot(mat1.melt[mat1.melt$mat %in% "MAT_1",], aes(x=Pos, y=Depth)) + geom_line() + facet_grid(sample~.) + 
  geom_segment(aes(y=100, yend=100, x=9951, xend=10800, color="MAT1_2_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) + 
  geom_segment(aes(y=100, yend=100,x=11159, xend=13083, color="MAT1_1_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) + 
  geom_segment(aes(y=100, yend=100, x=14590, xend=17898, color="SLA"), arrow = arrow(length = unit(0.1, "inches")), size=3) + theme_classic()

MAT2

ggplot(mat1.melt[mat1.melt$mat %in% "MAT_2",], aes(x=Pos, y=Depth)) + geom_line() + facet_grid(sample ~ .) + 
  geom_segment(aes(y=100, yend=100, x=9951, xend=10800, color="MAT1_2_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) + 
  geom_segment(aes(y=100, yend=100,x=11159, xend=13083, color="MAT1_1_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) + 
  geom_segment(aes(y=100, yend=100, x=14590, xend=17898, color="SLA"), arrow = arrow(length = unit(0.1, "inches")), size=3) + theme_classic()

Proportion of mating types per population

The idea is to test that the mating types are in a 1:1 proportion in the population in order to propose that sexual reproduction is occurring. Lets test that hypothesis:

library(poppr)

## Loading required package: adegenet

## Loading required package: ade4

## Registered S3 method overwritten by 'spdep':
##   method   from
##   plot.mst ape

## 
##    /// adegenet 2.1.3 is loaded ////////////
## 
##    > overview: '?adegenet'
##    > tutorials/doc/questions: 'adegenetWeb()' 
##    > bug reports/feature requests: adegenetIssues()

## Registered S3 method overwritten by 'pegas':
##   method      from
##   print.amova ade4

## This is poppr version 2.8.5. To get started, type package?poppr
## OMP parallel support: available

# Setting up population vectors:
lepto.snp <- readRDS("LeptoPNW.snpclone")

# Removing unused samples
mat.ann.raw <- mat.ann.raw[mat.ann.raw$sample %in% indNames(lepto.snp), ]

Mating proportion per site

pvals.mat <- lapply(levels(lepto.snp@strata$Site), function (x) {
  samples.site <- rownames(lepto.snp@strata[lepto.snp@strata$Site %in% x,])
  mat.sub <- mat.ann.raw[mat.ann.raw$sample %in% samples.site,]
  mat.tab <- table(mat.sub$mat) %>% unname
  max.prop <- max(mat.tab)/min(mat.tab) %>% round(digits = 2)
  expect <- nrow(mat.sub)/2
  chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect
  chisq.val <- (5 - expect)^2/5
  qchisq(chisq.val, df = 1)
  chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect + (table(mat.sub$mat)[2] - expect)^2/expect
  chisq.res <- chisq.test(table(mat.sub$mat), p = c(0.5,0.5))
  data.frame("Site"=x, "MAT1"=table(mat.sub$mat)[1] %>% unname, "MAT2"=table(mat.sub$mat)[2] %>% unname, "Proportions"=paste0(round(max.prop,digits = 2),":",1), "Chisq"=chisq.res$p.value)
}) %>% bind_rows()

## Warning in qchisq(chisq.val, df = 1): NaNs produced

## Warning in qchisq(chisq.val, df = 1): NaNs produced

## Warning in qchisq(chisq.val, df = 1): NaNs produced

pvals.mat %>%
  kable(escape = F) %>%
  kable_styling("hover", full_width = F) %>%
  column_spec(4, width = "3cm")

Site	MAT1	MAT2	Proportions	Chisq
MP	7	3	2.33:1	0.2059032
STF	24	23	1.04:1	0.8840280
WRTF	27	20	1.35:1	0.3072284
WSTF	44	6	7.33:1	0.0000001

Mating proportion per strand

pvals.mat <- lapply(levels(lepto.snp@strata$Stand), function (x) {
  samples.site <- rownames(lepto.snp@strata[lepto.snp@strata$Stand %in% x,])
  mat.sub <- mat.ann.raw[mat.ann.raw$sample %in% samples.site,]
  mat.tab <- table(mat.sub$mat) %>% unname
  max.prop <- max(mat.tab)/min(mat.tab) %>% round(digits = 2)
  expect <- nrow(mat.sub)/2
  chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect
  chisq.val <- (5 - expect)^2/5
  qchisq(chisq.val, df = 1)
  chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect + (table(mat.sub$mat)[2] - expect)^2/expect
  chisq.res <- chisq.test(table(mat.sub$mat), p = c(0.5,0.5))
  chisq.res$p.value
  data.frame("Stand"=x, "MAT1"=table(mat.sub$mat)[1] %>% unname, "MAT2"=table(mat.sub$mat)[2] %>% unname, "Proportions"=paste0(round(max.prop,digits = 2),":",1), "Chisq"=chisq.res$p.value)
}) %>% bind_rows()

## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect

## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect

## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect

## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect

## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect

## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect

pvals.mat %>%
  kable(escape = F) %>%
  kable_styling("hover", full_width = F) %>%
  column_spec(4, width = "3cm")

Stand	MAT1	MAT2	Proportions	Chisq
MP_MP	7	3	2.33:1	0.2059032
STF_1015	5	4	1.25:1	0.7388827
STF_1032	3	7	2.33:1	0.2059032
STF_O706	3	7	2.33:1	0.2059032
STF_O7A2	7	3	2.33:1	0.2059032
STF_O9A5	6	2	3:1	0.1572992
WRTF_5180	5	6	1.2:1	0.7630246
WRTF_5360	8	3	2.67:1	0.1316680
WRTF_5480	6	0	Inf:1	0.0143059
WRTF_5481B	3	8	2.67:1	0.1316680
WRTF_8800	5	3	1.67:1	0.4795001
WSTF_230	6	2	3:1	0.1572992
WSTF_700	7	1	7:1	0.0338949
WSTF_7280	13	0	Inf:1	0.0003115
WSTF_730	11	0	Inf:1	0.0009111
WSTF_770	7	3	2.33:1	0.2059032